Adaptive occupancy real-time predictive routing

ABSTRACT

The methods, apparatus, and systems described herein are designed to route customer communications to the best agent or best available agent. The methods include receiving a customer communication, predicting a demographic profile of the customer associated with the customer communication, monitoring real-time agent performance data and modifying a predetermined work threshold based on the real-time agent performance data, and providing a routing recommendation to route the customer to an agent based on the prediction and historical customer data for agents who have not exceeded the predetermined work threshold.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.14/818,082, filed Aug. 4, 2015, now allowed, which is a continuation ofU.S. application Ser. No. 14/518,423, filed Oct. 20, 2014, now U.S. Pat.No. 9,137,373, which is a continuation of U.S. application Ser. No.14/090,949, filed Nov. 26, 2013, now U.S. Pat. No. 8,867,733, which is acontinuation of U.S. application Ser. No. 14/049,082, filed Oct. 8,2013, now abandoned, which is a continuation of Ser. No. 13/828,154,filed Mar. 14, 2013, now U.S. Pat. No. 9,137,372, the entire contents ofeach of which is hereby incorporated herein its entirety by expressreference thereto.

TECHNICAL FIELD

The present disclosure generally relates to a method, apparatus, andsystem for routing customer communications, and more particularly toproviding routing recommendations based on predicted customerpersonalities and agent availability.

BACKGROUND OF THE DISCLOSURE

Call-routing ability and efficiency is important. The time it takes toconnect a caller to an agent affects customer satisfaction and hencebusiness image. Mistakes in routing, connecting callers for example tooverloaded centers or to agents not prepared to help with the client'sdifficulty or desire, is troublesome.

Automatic call distribution systems are known. Often an organizationdisseminates a single telephone number to its customers and to thepublic in general as a means of contacting the organization. As callsare directed to the organization from the public switch telephonenetwork, the automatic call distribution system directs the calls to itsagents based upon some type of criteria. For example, where all agentsare considered equal, the automatic call distributor may distribute thecalls based upon which agent has been idle the longest.

Automatic call distributors are used in communications handling centers,such as telephone call centers, that forward incoming communications,such as telephone calls, for processing by one of several associatedcall-handling agents. Other communications centers may be used toforward voice-over-internet protocol communications; electronic mailmessages; facsimiles or the like, to associated handling agents.

One concern in designing an automatic call distributor system isensuring that calls are efficiently routed to an agent, so as tominimize the amount of time that any particular call is placed on hold.One basic technique of minimizing on-hold time is to employ afirst-in/first-out call handling technique. The first-in/first-outtechnique requires that calls be routed to the next available agent inthe order in which the calls are received. In many cases, however, thefirst-in/first-out technique is not appropriate. For example, there maybe agents with specialized knowledge or expertise. Utilizing afirst-in/first-out technique in such a situation is inappropriatebecause a caller with a specific question related to a specific area maybe routed to an agent not having specialized knowledge in that area.Improvements in routing techniques and speeds are therefore needed.

SUMMARY

A customer calls, or otherwise communicates with a contact center. Usingidentifying origination data from the customer communication, thepersonality type of the customer is predicted. Agents available tohandle the customer communication are also determined and ranked. Arouting recommendation is provided based on the predicted personalitytype and the available agent's proficiency at handling customers withthe predicted personality type. In some instances, real-timecustomer-agent interaction may be analyzed to determine whether thecustomer communication should be re-routed to another agent.

The systems, apparatus, and methods disclosed herein may be used todistribute customer tasks or communications to the best available agenton duty at the moment based on personality type and other factors, whileexcluding all agents who have exceeded their work threshold. The presentdisclosure describes how to efficiently route customer communications,increase customer satisfaction, and maximize contact center performance.

In a first aspect, the invention encompasses a system for routingincoming customer tasks that includes a node comprising a processor anda computer readable medium operably coupled thereto, the computerreadable medium comprising a plurality of instructions stored inassociation therewith that are accessible to, and executable by, theprocessor, where the plurality of instructions includes, instructions,that when executed, receive a customer task, instructions, that whenexecuted, return a list of available agents, wherein the list ofavailable agents excludes agents that exceed a predetermined workthreshold, and instructions, that when executed, provide a routingrecommendation modified by (including being based on) predicted customerpersonality type, task type, customer data, agent data, or a combinationthereof.

In a second aspect, the invention encompasses a system for routingincoming customer communications, that includes a database module toassociate identifying origination data of a customer with a predictionof a personality type of the customer, a governor module to monitoragent work load and provide a list of available agents, wherein the listof available agents excludes agents that exceed a predetermined workthreshold, and a routing module to match customer communications toavailable agents based on the predicted customer personality type andthe available agents' proficiency at handling customers with thepredicted personality type.

In a third aspect, the invention encompasses a method for routingincoming customer communications that includes, receiving a customercommunication, predicting a personality type of the customer associatedwith the customer communication, providing a list of available agents topermit routing of the customer communication, wherein the list ofavailable agents excludes agents that have exceeded a predetermined workthreshold, and providing a routing recommendation based on thepersonality type prediction and the available agent's proficiency athandling customers with the predicted personality type.

In a fourth aspect, the invention encompasses a computer readable mediumcomprising a plurality of instructions that includes instructions, thatwhen executed, receive a customer communication, instructions, that whenexecuted, predict a personality type of the customer associated with thecustomer communication, instructions, that when executed, determinewhich agents are available by calculating occupancy for each agent froma list of agents and by excluding all agents from the list who exceed apredetermined work threshold, instructions, that when executed, rank theavailable agents based on their proficiency at handling customers withthe predicted personality profile, and instructions, that when executed,provide a recommendation that directs the customer communication to thebest available agent based on the ranking, task type, customer data,agent data, or a combination thereof.

In a fifth aspect, the present disclosure encompasses a system forrouting incoming customer tasks that includes a node comprising aprocessor and a computer readable medium operably coupled thereto, thecomputer readable medium comprising a plurality of instructions storedin association therewith that are accessible to, and executable by, theprocessor, where the plurality of instructions includes: instructionsthat, when executed, identify origination data for a customer contactinga contact center with a customer task; instructions that, when executed,determine a predicted personality type of the customer based on theidentified origination data; and instructions that, when executed,provide a routing recommendation to route the customer to an agent basedon the predicted customer personality type and historical customer data.

In a sixth aspect, the present disclosure encompasses a system forrouting incoming customer communications that includes a database moduleto associate identifying origination data of a customer contacting acontact center, an analytics module to determine a predicted personalitytype of the customer based on the identified origination data, and arouting module to match a customer communication to an available agentbased on the predicted customer personality type and the historicalcustomer data.

In a seventh aspect, the present disclosure encompasses a method forrouting incoming customer communications that includes receiving acustomer communication, predicting a personality type of the customerassociated with the customer communication, and providing a routingrecommendation to route the customer to an agent based on thepersonality type prediction and historical customer data. In a preferredembodiment, the method further includes providing a list of availableagents to permit routing of the customer communication, wherein the listof available agents excludes agents that have exceeded a predeterminedwork threshold.

In an eighth aspect, the present disclosure encompasses a non-transitorycomputer readable medium including a plurality of instructions thatinclude instructions, that when executed, receive a customercommunication and identify origination data for the customer contactingthe contact center; instructions, that when executed, predict apersonality type of the customer associated with the customercommunication based on the identified origination data; andinstructions, that when executed, provide a routing recommendation thatdirects the customer communication to the best available agent based onthe predicted customer personality type and historical customer data.

In a ninth aspect, the present disclosure encompasses a systemconfigured to route incoming customer tasks based on agent performance,including a node that includes a processor and a non-transitory computerreadable medium operably coupled thereto, the non-transitory computerreadable medium comprising a plurality of instructions stored inassociation therewith that are accessible to, and executable by, theprocessor, where the plurality of instructions includes: instructionsthat, when executed, identify origination data for a customer contactinga contact center with a customer task, instructions that, when executed,monitor real-time agent performance data and modify a predetermined workthreshold based on the real-time agent performance data, instructionsthat, when executed, provide a routing recommendation to a communicationdistributor to route the customer to an agent based on historicalcustomer data, wherein the agent has not exceeded the predetermined workthreshold, and instructions that, when executed, route the communicationvia the communication distributor to the agent based on the routingrecommendation.

In a tenth aspect, the present disclosure encompasses a systemconfigured to route incoming customer communications that includes adatabase module to associate identifying origination data of a customercontacting a contact center, a governor module to monitor real-timeagent performance data and modify a predetermined work threshold basedon the real-time agent performance data, a routing processor to match acustomer communication to an available agent based on historicalcustomer data for the customer, wherein the available agent has notexceeded the predetermined work threshold, and a communicationdistributor that routes the customer communication to the availableagent based on the routing match.

In an eleventh aspect, the present disclosure encompasses a method forrouting incoming customer communications, which includes receiving, byone or more processors, a customer communication, monitoring, by the oneor more processors, real-time agent performance data and modifying apredetermined work threshold based on the real-time agent performancedata, and providing, by the one or more processors, a routingrecommendation to a communication distributor to route the customer toan agent based on historical customer data, wherein the agent has notexceeded the predetermined work threshold.

In a twelfth aspect, the present disclosure encompasses a systemconfigured to simulate a real-time predictive routing system to routeincoming customer tasks, including a node that includes a processor anda non-transitory computer readable medium operably coupled thereto, thenon-transitory computer readable medium comprising a plurality ofinstructions stored in association therewith that are accessible to, andexecutable by, the processor, where the plurality of instructionsincludes: instructions that, when executed, identify origination datafor a customer contacting a contact center with a customer task,instructions that, when executed, monitor agent workload and stopassigning customer tasks to each agent at any time that the agent hasexceeded a predetermined work threshold, instructions that, whenexecuted, provide a routing recommendation to route the customer to anagent based on historical customer data, wherein the agent has notexceeded the predetermined work threshold, instructions that, whenexecuted, route the customer to an agent via a communicationsdistributor when the agent has not exceeded the predetermined workthreshold, instructions that, when executed, create an actual routingsummary of the routing, and instructions that, when executed, compare arecommended routing summary based on the routing recommendations over aperiod of time to the actual routing summary over the period of time,and display the comparison to a user on a user display.

In a thirteenth aspect, the present disclosure encompasses a systemconfigured to route incoming customer communications, including adatabase module to associate identifying origination data of a customercontacting a contact center, a governor module to monitor real-timeagent performance data and modify a predetermined work threshold basedon the real-time agent performance data, a routing processor to providea routing recommendation to route a customer communication to anavailable agent based on historical customer data for the customer,wherein the available agent has not exceeded the predetermined workthreshold, a communication distributor that routes the customercommunication to the available agent based on the routing match andcreates an actual routing summary of the routes over a period of time,and a simulation module that compares a recommended routing summarybased on the routing recommendations over the period of time to theactual routing summary over the period of time, and displays thecomparison to a user on a user display.

In a fourteenth aspect, the present disclosure encompasses a method forrouting incoming customer communications, which includes receiving, byone or more processors, a customer communication, monitoring, by the oneor more processors, real-time agent performance data and modifying apredetermined work threshold based on the real-time agent performancedata, providing, by the one or more processors, a routing recommendationto route the customer to an agent based on historical customer data,wherein the agent has not exceeded the predetermined work threshold,routing the customer to an agent when the agent has not exceeded thepredetermined work threshold, creating an actual routing summary of oneor more of the routings over a period of time, and comparing arecommended routing summary based on the routing recommendations to theactual routing summary over the period of time, and display thecomparison to a user.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is best understood from the following detaileddescription when read with the accompanying figures. It is emphasizedthat, in accordance with the standard practice in the industry, variousfeatures are not drawn to scale. In fact, the dimensions of the variousfeatures may be arbitrarily increased or reduced for clarity ofdiscussion.

FIG. 1 is a simplified block diagram of an embodiment of a contactcenter according to various aspects of the present disclosure.

FIG. 2 is a more detailed block diagram of the contact center of FIG. 1according to aspects of the present disclosure.

FIG. 3 is simplified block diagram of an embodiment of a contact center,analytics center, and a system for routing customer communicationsaccording to various aspects of the present disclosure.

FIG. 4 is a flowchart illustrating a preferred method of routingcustomer communications according to aspects of the present disclosure.

FIG. 5 is a block diagram of a computer system suitable for implementingone or more components in FIG. 3 according to one embodiment of thepresent disclosure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present disclosure advantageously provides for methods for routingincoming customer communications. These methods typically includereceiving a customer communication, predicting a personality type of thecustomer associated with the customer communication, providing a list ofavailable agents to permit routing of the customer communication, andproviding a routing recommendation, which is typically based on thepersonality type prediction and the available agent's proficiency athandling customers with the predicted personality type. Systems andapparatuses for carrying out these methods are also part of the presentdisclosure. An exemplary system to route incoming customer tasksincludes, for example, a node including a processor and a computerreadable medium operably coupled thereto, the computer readable mediumcomprising a plurality of instructions stored in association therewiththat are accessible to, and executable by, the processor, where theplurality of instructions includes instructions, that when executed,receive a customer task, provide a list of available agents (e.g.,wherein the list of available agents excludes agents that exceed apredetermined work threshold), and provide a routing recommendationmodified by (including being based on) predicted customer personalitytype, task type, customer data, agent data, or a combination thereof.

For the purposes of promoting an understanding of the principles of thepresent disclosure, reference will now be made to the embodimentsillustrated in the drawings, and specific language will be used todescribe the same. It is nevertheless understood that no limitation tothe scope of the disclosure is intended. Any alterations and furthermodifications to the described devices, systems, and methods, and anyfurther application of the principles of the present disclosure arefully contemplated and included within the present disclosure as wouldnormally occur to one of ordinary skill in the art to which thedisclosure relates. In particular, it is fully contemplated that thefeatures, components, and/or steps described with respect to oneembodiment may be combined with the features, components, and/or stepsdescribed with respect to other embodiments of the present disclosure.For the sake of brevity, however, the numerous iterations of thesecombinations will not be described separately.

FIG. 1 is a simplified block diagram of an embodiment of a contactcenter 100 according to various aspects of the present disclosure. A“contact center” as used herein can include any facility or systemserver suitable for receiving and recording electronic communicationsfrom customers. Such communications can include, for example, telephonecalls, facsimile transmissions, e-mails, web interactions, voice over IP(“VoIP”) and video. Various specific types of communicationscontemplated through one or more of these channels include, withoutlimitation, email, SMS data (e.g., text), tweet, instant message,web-form submission, smartphone app, social media data, and web contentdata (including but not limited to internet survey data, blog data,microblog data, discussion forum data, and chat data), etc. In variousaspects, real-time communication, such as voice, video, or both, ispreferably included. It is contemplated that these communications may betransmitted by and through any type of telecommunication device and overany medium suitable for carrying data. For example, the communicationsmay be transmitted by or through telephone lines, cable, or wirelesscommunications. As shown in FIG. 1, the contact center 100 of thepresent disclosure is adapted to receive and record varying electroniccommunications and data formats that represent an interaction that mayoccur between a customer (or caller) and a contact center agent duringfulfillment of a customer and agent transaction. In one embodiment, thecontact center 100 records all of the customer calls in uncompressedaudio formats. In the illustrated embodiment, customers may communicatewith agents associated with the contact center 100 via multipledifferent communication networks such as a public switched telephonenetwork (PSTN) 102 or the Internet 104. For example, a customer mayinitiate an interaction session through traditional telephones 106, afax machine 108, a cellular (i.e., mobile) telephone 110, a personalcomputing device 112 with a modem, or other legacy communication devicevia the PSTN 102. Further, the contact center 100 may acceptinternet-based interaction sessions from personal computing devices 112,VoIP telephones 114, and internet-enabled smartphones 116 and personaldigital assistants (PDAs).

Often, in contact center environments such as contact center 100, it isdesirable to facilitate routing of customer contacts, particularly basedon agent availability and prediction of personality type of the customeroccurring in association with a customer interaction, be it atelephone-based interaction, a web-based interaction, or other type ofelectronic interaction over the PSTN 102 or Internet 104. Traditionally,limited categories of customer data are used to create predictivemodels. As a result, such models tend not to be as accurate as possiblebecause of limited data inputs and because of the heterogeneous natureof interaction data collected across multiple different communicationchannels. As one of ordinary skill in the art would recognize, theillustrated example of communication channels associated with a contactcenter 100 in FIG. 1 is just an example, and the contact center mayaccept customer interactions, and other analyzed interaction informationand/or routing recommendations from an analytics center, through variousadditional and/or different devices and communication channels whetheror not expressly described herein.

For example, in some embodiments, internet-based interactions and/ortelephone-based interactions may be routed through an analytics center120 before reaching the contact center 100 or may be routedsimultaneously to the contact center and the analytics center (or evendirectly and only to the contact center). In some instances, theanalytics center 120 is a third-party analytics company that capturesmulti-channel interaction data associated with the contact center 100and applies predictive analytics to the data to generate actionableintelligence for the contact center. For example, the analytics center120 may provide a routing recommendation according to the presentdisclosure, a database module to associate identifying origination dataof a customer and/or generate a prediction of a personality type of thecustomer, a governor module to monitor agent work load and provide alist of available agents, a routing module to match customercommunications to available agents based on the predicted customerpersonality type and the available agents' proficiency at handlingcustomers with the predicted personality type, or any combinationthereof, as well as providing all of the above functionality. Also, insome embodiments, internet-based interactions may be received andhandled by a marketing department associated with either the contactcenter 100 or analytics center 120. The analytics center 120 may becontrolled by the same entity or a different entity than the contactcenter 100. Further, the analytics center 120 may be a part of, orindependent of, the contact center 100.

FIG. 2 is a more detailed block diagram of an embodiment of the contactcenter 100 according to aspects of the present disclosure. As shown inFIG. 2, the contact center 100 is communicatively coupled to the PSTN102 via a distributed private branch exchange (PBX) switch 130. The PBXswitch 130 provides an interface between the PSTN 102 and a local areanetwork (LAN) 132 within the contact center 100. In general, the PBXswitch 130 connects trunk and line station interfaces of the PSTN 102 tocomponents communicatively coupled to the LAN 132. The PBX switch 130may be implemented with hardware or virtually. A hardware-based PBX maybe implemented in equipment located local to the user of the PBX system.In contrast, a virtual PBX may implemented in equipment located at acentral telephone service provider that delivers PBX functionality as aservice over the PSTN 102. Additionally, in one embodiment, the PBXswitch 130 may be controlled by software stored on a telephony server134 coupled to the PBX switch. In another embodiment, the PBX switch 130may be integrated within telephony server 134. The telephony server 134incorporates PBX control software to control the initiation andtermination of connections between telephones within the contact center100 and outside trunk connections to the PSTN 102. In addition, thesoftware may monitor the status of all telephone stations coupled to theLAN 132 and may be capable of responding to telephony events to providetraditional telephone service. In certain embodiments, this may includethe control and generation of the conventional signaling tones includingwithout limitation dial tones, busy tones, ring back tones, as well asthe connection and termination of media streams between telephones onthe LAN 132. Further, the PBX control software may programmaticallyimplement standard PBX functions such as the initiation and terminationof telephone calls, either across the network or to outside trunk lines,the ability to put calls on hold, to transfer, park and pick up calls,to conference multiple callers, and to provide caller ID information.Telephony applications such as voice mail and auto attendant may beimplemented by application software using the PBX as a network telephonyservices provider.

In one embodiment, the telephony server 134 includes a trunk interfacethat utilizes conventional telephony trunk transmission supervision andsignaling protocols required to interface with the outside trunkcircuits from the PSTN 102. The trunk lines carry various types oftelephony signals such as transmission supervision and signaling, audio,fax, or modem data to provide plain old telephone service (POTS). Inaddition, the trunk lines may carry other communication formats such T1,ISDN or fiber service to provide telephony or multimedia data images,video, text or audio.

The telephony server 134 includes hardware and software components tointerface with the LAN 132 of the contact center 100. In one embodiment,the LAN 132 may utilize IP telephony, which integrates audio and videostream control with legacy telephony functions and may be supportedthrough the H.323 protocol. H.323 is an International TelecommunicationUnion (ITU) telecommunications protocol that defines a standard forproviding voice and video services over data networks. H.323 permitsusers to make point-to-point audio and video phone calls over a localarea network. IP telephony systems can be integrated with the publictelephone system through an IP/PBX-PSTN gateway, thereby allowing a userto place telephone calls from an enabled computer. For example, a callfrom an IP telephony client within the contact center 100 to aconventional telephone outside of the contact center would be routed viathe LAN132 to the IP/PBX-PSTN gateway. The IP/PBX-PSTN gateway wouldthen translate the H.323 protocol to conventional telephone protocol androute the call over the PSTN 102 to its destination. Conversely, anincoming call from a customer over the PSTN 102 may be routed to theIP/PBX-PSTN gateway, which translates the conventional telephoneprotocol to H.323 protocol so that it may be routed to a VoIP-enablephone or computer within the contact center 100.

The contact center 100 is further communicatively coupled to theInternet 104 via hardware and software components within the LAN 132.One of ordinary skill in the art would recognize that the LAN 132 andthe connections between the contact center 100 and external networkssuch as the PSTN 102 and the Internet 104 as illustrated by FIG. 2 havebeen simplified for the sake of clarity and the contact center mayinclude various additional and/or different software and hardwarenetworking components such as routers, switches, gateways, networkbridges, hubs, and legacy telephony equipment.

In various embodiments, the contact center 100 includes a communicationdistributor that distributes customer communications or tasks to agents.Generally, the communication distributor is part of a switching systemdesigned to receive customer communications and queue them. In addition,the communication distributor distributes communications to agents orspecific groups of agents according to a prearranged scheme.

As shown in FIG. 2, the contact center 100 includes a plurality of agentworkstations 140 that enable agents employed by the contact center toengage in customer interactions over a plurality of communicationchannels. In one embodiment, each agent workstation 140 may include atleast a telephone and a computer workstation. In other embodiments, eachagent workstation 140 may include a computer workstation that providesboth computing and telephony functionality. Through the workstations140, the agents may engage in telephone conversations with the customer,respond to email inquiries, receive faxes, engage in instant messageconversations, respond to website-based inquires, video chat with acustomer, and otherwise participate in various customer interactionsessions across one or more channels. Further, in some embodiments, theagent workstations 140 may be remotely located from the contact center100, for example, in another city, state, or country. Alternatively, insome embodiments, an agent may be a software-based applicationconfigured to interact in some manner with a customer. An exemplarysoftware-based application as an agent is an online chat programdesigned to interpret customer inquiries and respond with pre-programmedanswers.

The contact center 100 further includes a contact center control system142 that is generally configured to provide recording, voice analysis,behavioral analysis, storage, and other processing functionality to thecontact center. In the illustrated embodiment, the contact centercontrol system 142 is an information handling system such as a computer,server, workstation, mainframe computer, or other suitable computingdevice. In other embodiments, the control system 142 may be a pluralityof communicatively coupled computing devices coordinated to provide theabove functionality for the contact center 100. The control system 142includes a processor 144 that is communicatively coupled to a systemmemory 146, a mass storage device 148, and a communication module 150.The processor 144 can be any custom made or commercially availableprocessor, a central processing unit (CPU), an auxiliary processor amongseveral processors associated with the control system 142, asemiconductor-based microprocessor (in the form of a microchip or chipset), a macroprocessor, a collection of communicatively coupledprocessors, or any device for executing software instructions. Thesystem memory 146 provides the processor 144 with non-transitory,computer-readable storage to facilitate execution of computerinstructions by the processor. Examples of system memory may includerandom access memory (RAM) devices such as dynamic RAM (DRAM),synchronous DRAM (SDRAM), solid state memory devices, and/or a varietyof other memory devices known in the art. Computer programs,instructions, and data, such as known voice prints, may be stored on themass storage device 148. Examples of mass storage devices may includehard discs, optical disks, magneto-optical discs, solid-state storagedevices, tape drives, CD-ROM drives, and/or a variety other mass storagedevices known in the art. Further, the mass storage device may beimplemented across one or more network-based storage systems, such as astorage area network (SAN). The communication module 150 is operable toreceive and transmit contact center-related data between local andremote networked systems and communicate information such as customerinteraction recordings between the other components coupled to the LAN132. Examples of communication modules may include Ethernet cards,802.11 WiFi devices, cellular data radios, and/or other suitable devicesknown in the art. The contact center control system 142 may furtherinclude any number of additional components, which are omitted forsimplicity, such as input and/or output (I/O) devices (or peripherals),buses, dedicated graphics controllers, storage controllers, buffers(caches), and drivers. Further, functionality described in associationwith the control system 142 may be implemented in software (e.g.,computer instructions), hardware (e.g., discrete logic circuits,application specific integrated circuit (ASIC) gates, programmable gatearrays, field programmable gate arrays (FPGAs), etc.), or a combinationof hardware and software.

According to one aspect of the present disclosure, the contact centercontrol system 142 is configured to record, collect, and analyzecustomer voice data and other structured and unstructured data, andother tools may be used in association therewith to increase efficiencyand efficacy of the contact center. As an aspect of this, the controlsystem 142 is operable to record unstructured interactions betweencustomers and agents occurring over different communication channelsincluding without limitation telephone conversations, email exchanges,website postings, social media communications, smartphone application(i.e., app) communications, fax messages, instant message conversations.For example, the control system 142 may include a hardware orsoftware-based recording server to capture the audio of a standard orVoIP telephone connection established between an agent workstation 140and an outside customer telephone system. Further, the audio from anunstructured telephone call or video conference session may betranscribed manually or automatically and stored in association with theoriginal audio or video. In one embodiment, multiple communicationchannels (i.e., multi-channel) may be used according to the invention,either in real-time to collect information, for evaluation, or both. Forexample, control system 142 can receive, evaluate, and store telephonecalls, emails, and fax messages. Thus, multi-channel can refer tomultiple channels of interaction data, or analysis using two or morechannels, depending on the context herein.

In addition to unstructured interaction data such as interactiontranscriptions, the control system 142 is configured to capturedstructured data related to customers, agents, and their interactions.For example, in one embodiment, a “cradle-to-grave” recording may beused to record all information related to a particular telephone callfrom the time the call enters the contact center to the later of: thecaller hanging up or the agent completing the transaction. All or aportion of the interactions during the call may be recorded, includinginteraction with an interactive voice response (IVR) system, time spenton hold, data keyed through the caller's key pad, conversations with theagent, and screens displayed by the agent at his/her station during thetransaction. Additionally, structured data associated with interactionswith specific customers may be collected and associated with eachcustomer, including without limitation the number and length of callsplaced to the contact center, call origination information, reasons forinteractions, outcome of interactions, average hold time, agent actionsduring interactions with customer, manager escalations during calls,types of social media interactions, number of distress events duringinteractions, survey results, and other interaction information. Inaddition to collecting interaction data associated with a customer, thecontrol system 142 is also operable to collect biographical profileinformation specific to a customer including without limitation customerphone number, account/policy numbers, address, employment status,income, gender, customer “value” data (i.e., customer tenure, moneyspent as customer, etc.), personality type (as determined by pastinteractions), and other relevant customer identification and biologicalinformation. The control system 142 may also collect agent-specificunstructured and structured data including without limitation agentpersonality type, gender, language skills, performance data (e.g.,customer retention rate, etc.), tenure and salary data, training level,average hold time during interactions, manager escalations, agentworkstation utilization, and any other agent data relevant to contactcenter performance. Additionally, one of ordinary skill in the art wouldrecognize that the types of data collected by the contact center controlsystem 142 that are identified above are simply examples and additionaland/or different interaction data, customer data, agent data, andtelephony data may be collected and processed by the control system 142.The control system 142 may store recorded and collected interaction datain a database 152, including customer data and agent data. In certainembodiments, agent data, such as agent scores for dealing withcustomers, are updated daily.

The control system 142 may store recorded and collected interaction datain a database 152. The database 152 may be any type of reliable storagesolution such as a RAID-based storage server, an array of hard disks, astorage area network of interconnected storage devices, an array of tapedrives, or some other scalable storage solution located either withinthe contact center or remotely located (i.e., in the cloud). Further, inother embodiments, the contact center control system 142 may have accessnot only to data collected within the contact center 100 but also datamade available by external sources such as a third party database 154.In certain embodiments, the control system 142 may query the third partydatabase for customer data such as credit reports, past transactiondata, and other structured and unstructured data.

Additionally, in some embodiments, an analytics system 160 may alsoperform some or all of the functionality ascribed to the contact centercontrol system 142 above. For instance, the analytics system 160 mayrecord telephone and internet-based interactions, perform behavioralanalyses, predict customer personalities, and perform other contactcenter-related computing tasks. The analytics system 160 may beintegrated into the contact center control system 142 as a hardware orsoftware module and share its computing resources 144, 146, 148, and150, or it may be a separate computing system housed, for example, inthe analytics center 120 shown in FIG. 1. In the latter case, theanalytics system 160 includes its own processor and non-transitorycomputer-readable storage medium (e.g., system memory, hard drive, etc.)on which to store predictive analytics software and other softwareinstructions.

The multi-channel interaction data collected in the context of thecontrol center 100 may be subject to a linguistic-based psychologicalbehavioral model to assess the personality of customers and agentsassociated with the interactions. For example, such a behavioral modelmay be applied to the transcription of a telephone call, instant messageconversation, or email thread, between a customer and agent to gaininsight into why a specific outcome resulted from the interaction.

In one embodiment, interaction data is mined for behavioral signifiersassociated with a linguistic-based psychological behavioral model. Inparticular, the contact center control system 142 searches for andidentifies text-based keywords (i.e., behavioral signifiers) relevant toa predetermined psychological behavioral model. In a preferredembodiment, multi-channels are mined for such behavioral signifiers.

FIG. 3 illustrates an exemplary predictive routing system 300operatively associated with contact center 100. In one embodiment, partsor the whole of predictive routing system 300 is integrated into contactcenter 100. In another embodiment, parts or the whole of predictiverouting system 300 is operated separately from contact center 100, suchas by a processing/analytics company (i.e., in this unshown embodiment,the contact center 100 may be replaced with an analytics center 120 inwhole or in part), and predictive routing system 300 provides routingrecommendations to contact center 100. As shown, predictive routingsystem 300 includes database module 305, governor module 310, routingmodule 315, and analytics module 320.

As shown, database module 305 receives customer communication data fromcontact center 100 and associates identifying origination data of thecustomer with a prediction of what personality type the customer islikely to be. Identifying origination data typically includes a contactnumber or network address, or any combination thereof. The contactnumber may include at least one of a telephone number, a text messagenumber, short message service (SMS) number, multimedia message service(MMS) number, or a combination thereof. The network address can includeat least one of an email address, electronic messaging address, voiceover IP address, IP address, social media identifier (e.g., Facebookidentifier, Twitter identifier, chat identifier), or a combinationthereof. These identifiers are associated with personality types basedon the linguistic model.

It is well known that certain psychological behavioral models have beendeveloped as tools to evaluate and understand how and/or why one personor a group of people interacts with another person or group of people.The Process Communication ModelTM (“PCM”) developed by Dr. Taibi Kahleris a preferred example of one such behavioral model. Specifically, PCMpresupposes that all people fall primarily into one of six basicpersonality types: Reactor, Workaholic, Persister, Dreamer, Rebel andPromoter. Although each person is one of these six types, all peoplehave parts of all six types within them arranged like a “six-tierconfiguration.” Each of the six types learns differently, is motivateddifferently, communicates differently, and has a different sequence ofnegative behaviors in which they engage when they are in distress.Importantly each PCM personality type responds positively or negativelyto communications that include tones or messages commonly associatedwith another of the PCM personality types. Thus, an understanding of acommunicant's PCM personality type offers guidance as to an appropriateresponsive tone or message. Exemplary methods of applying apsychological behavioral model to contact center communications aredescribed in U.S. Pat. Nos. 7,995,717 and 8,094,803, and U.S. patentapplication Ser. No. 13/782,522, filed Mar. 1, 2013, entitled“Customer-Based Interaction Outcome Prediction Methods and System,” theentire contents of each of which is incorporated herein in its entiretyby express reference thereto.

In one embodiment, the prediction of customer personality type is basedon past calls or communications to that contact center and otherorganizations. For instance, the prediction can be based on previoustransactions between the customer and contact center, the answers tomenu choices, past purchase history, past calling history, past surveyresponses, etc. This prediction can be created and/or stored in thedatabase module 305 from past interaction with the customer.

The database module 305 contains the aggregated summary of scores acrossthe six personality types in a linguistic model and predicts whichpersonality type the customer is most likely to be. The aggregatedsummary of scores weighs certain communications differently to predictthe personality type of the customer in one embodiment. For example, ifthere are multiple calls from a single telephone number, more recentcalls are given more weight than older calls. Also, the time of day canbe taken into account to predict personality type of the customer ifmore than one personality type is associated with a single telephonenumber. For example, if the telephone number is associated with anemotions based customer during the day, and a thoughts based customer atnight, the database module 305 can return a customer personality typeprediction based on that pattern.

In various embodiments, one or more copies of the database module 305may be housed close to or in the contact center 100 to decrease the timeneeded to transfer the information from the database module 305 to therouting module 315 (when the routing module 315 is integrated in contactcenter 100), and to help minimize router decision time. The original maybe stored at an analytics center or datafarm site. Because the databasemodule 305 is typically very large, the data should be carefullystructured so that the database module 305 can return results within avery short period of time, as the data returned from the database module305 is used to route the customer communication or task to an agent.When the original or a copy are stored near the contact center 100, thedatabase module 305 may be mirrored at the contact center 100 to improveresult times, and any copies may be updated periodically (e.g., weekly)with new customer data. In some embodiments, the database module 305 isupdated and copied out every night.

The governor module 310 monitors agent workload and stops assigningcustomer tasks to agents once agents have exceeded a predetermined workthreshold. This can be based on, e.g., legal requirements, such as amaximum permitted workweek, agent preference, determination of anoptimum maximum threshold beyond which performance degrades, or thelike, or any combination thereof. In some embodiments, governor module310 is in communication with an agent queue of contact center 100. Thegovernor module 310 calculates the amount of utilization time for eachagent, so that once agents have reached a predetermined work thresholdthey will be taken out of the queue for assignment by the routing module315 until their occupancy level drops below the predetermined workthreshold. In some embodiments, the utilization calculations are done inreal-time. “Utilization time,” as used herein, means the time agentsspend communicating with a customer and time spent doing additionalcustomer related tasks after the communication.

Occupancy level is calculated using the following equation:

Utilization time/logged in time.

When the governor module 310 is queried by the contact center to returna list of available agents for an incoming customer task, agents thatare above the predetermined work threshold are excluded from the groupconsidered for the task, and the remaining agents are ranked. In certainembodiments, the agents are ranked based on their proficiency forhandling customers with the predicted personality type and/or the typeof task.

In one embodiment, when all agents are working above the predeterminedwork threshold, a routing recommendation may not be provided. Instead, asupervisor or other authorized user may raise the predetermined workthreshold to a higher second predetermined work threshold. In certainembodiments, the authorized user has the ability to adjust the governormodule 310 at any time. A routing recommendation may then be providedbased on the higher second predetermined work threshold.

In another embodiment, the governor module 310 recognizes that allagents are above the occupancy level and continues to make routingrecommendations. The governor module 310 can automatically raise thepredetermined threshold to a higher second predetermined work thresholdthat is triggered when all agents are above the initial predeterminedwork threshold.

The governor module 310 dynamically determines occupancy rate in realtime, or near-real time. For example, near-real time may be necessitatedby communication delays between inputs from a contact center and receiptand processing by an analytics center, causing a delay, e.g., of about 1to 10 seconds. Once an agent's occupancy rate falls below the adaptedthreshold, that agent will be returned to the queue. For example, if thepredetermined work threshold is 75%, and an agent's occupancy level is85%, that agent will be taken out of the queue. Once the agent'soccupancy level is below 75%, the agent is placed back in the queue bythe governor module.

In various embodiments, the governor module 315 is integrated with acontact center's existing environment, communication distributorhardware, and software. The governor module 315 does not handle callcontrol or reserve agents, but informs the communication distributor ofa contact center which agents should be taken out of the queue. Thegovernor module 315 tracks agent utilization for all agents in the queueand measures agent availability and agent occupancy.

In certain embodiments, the governor module 315 allows the occupancylevel to be adjusted to a customized level that is called the “adaptedthreshold.” The industry standard is presently about an 80-85% adaptedthreshold for full utilization. The occupancy level customization forthe adapted threshold also allows occupancy to be attributed indifferent ways for different types of incoming customer tasks. Forexample, agents receiving complaint calls can have a different thresholdthan sales call agents. In addition, the threshold can be setdifferently based on agent skills and communication type, so that topquartile sales agents will be worked to 85% occupancy and the rest ofthe agents to 80% occupancy or some other lower occupancy. The thresholdmay also be set based on personality type, so that the governor module315 may use a different threshold if there is an 80% utilized agent, butis a top quartile thoughts-based agent.

The routing module 315 matches incoming customer tasks and their relatedcustomer personality type predictions with agents made available by thegovernor module 310. The match-ups can be made based on the customerpersonality type prediction and information from an agent database thatincludes the agent's proficiency scores for handling customers with thatpersonality type. The match-ups can also be made based on a number ofother factors, such as the type of task, the agent's training, and theagent's workload.

In one embodiment, routing module 315 is in communication with thecommunication distributor of the contact center. Routing module 315provides a routing recommendation to the communication distributor,which can then distribute the customer task to the best available agent.

In certain embodiments, the routing module 315 provides a route summaryincluding the number of routes produced, the number of times therecommended routes were adopted, and an estimate of the amount of moneysaved. In some embodiments, the routing module 315 provides real-timehourly and daily reporting. The reporting function of the routing module315 allows the contact center to see that the routing module 315 isactually providing routing recommendations for routing customercontacts, rather than having a default system (e.g., communicationdistributor of the contact center) route the customer contacts.

In other embodiments, the routing module 315 may include a simulatorthat shows simulated routing on top of the default system, so that thecontact center can compare the default system to the routing module 315.

Analytics module 330 analyzes customer-agent interaction during thecustomer task. If the task involves speaking or other forms ofvoice-based communication, snippets of that communication are sent to ananalytics server to analyze the task interaction as it is happening.This gives the agent more information about the customer and provides amore accurate secondary routing personality prediction so that, if thetask requires additional routing during the customer contact, therouting module 315 or an agent can use that information to providerouting recommendations regarding the best available agent. Informationthat may be determined by the analytics module 320 includes, withoutlimitation, personality type of the customer, engagement, state of mind,distress, life events, and purpose of contact/task.

An exemplary method 400 of routing customer tasks or communications willnow be described with respect to FIG. 4. At step 402, a customercommunication or task is received at contact center 100. Again in FIG.4, the contact center 100 in one embodiment may be replaced by, or beassociated with, an analytics center 120 as seen in FIG. 3. Thecommunication type may include voice calls, voice over IP, facsimiles,emails, web page submissions, internet chat sessions, wireless messages(e.g., text messages such as SMS (short messaging system) messages orpaper messages), short message service (SMS), multimedia message service(MMS), or social media (e.g., Facebook identifier, Twitter identifier,etc.), IVR telephone sessions, voicemail messages (including emailedvoice attachments), or any combination thereof.

The database module 305 then receives a request from the contact center100 including identifying origination data of the customer. At step 404,the database module 305 associates the identifying origination data witha prediction of the personality type of the customer. The databasemodule 305 accesses and retrieves customer characteristics and scoresand provides the governor module 310 and routing module 315 with thisinformation. In one embodiment, it provides a prediction of the customerpersonality type.

At step 406, the governor module 310 determines the occupancy level ofagents, e.g., by obtaining agent data from the contact center 100. Thegovernor module 310 dynamically monitors occupancy level of the agentsto determine availability and addresses the real-time performancemetrics of the agent. This real-time (or near-real time) dynamic data istypically used to select a destination for the customer communication.Unlike conventional sequential routing schemes, preference in thisembodiment is based on both customer information and agent availability.

At step 408, the governor module 310 prepares a list of available agentsand ranks the available agents based on certain selected criteria, e.g.,agent's proficiency in handling customers with the predicted personalitytype. Available agents are those agents who have not exceeded apredetermined work threshold. This list of available agents is providedto routing module 315.

At step 410, the routing module 315 provides a routing recommendationthat matches the customer communication to the best available agent.Based on the customer personality prediction type, available agents,agent data, task type (which may be from IVR), customer data, customercontact events, environmental events, etc., the routing module 315provides the communication distributor of the contact center with theavailable agent best suited to take the customer communication. Agentdata includes, but is not limited to agent performance metrics, tenure,agent personality type analytics scores, and other data about the agent.Customer data includes, but is not limited to, customer ID, accounthistory with the contact center, customer contact frequency or history(including prior instances of distress), and other relevant availablecustomer attributes.

The routing module 315 may make routing decisions based on comparingvarious customer data and agent data, which may include, e.g.,performance based data, demographic data, psychographic data, and otherbusiness-relevant data. The routing module 315 assesses the skills ofavailable agents to establish which agents possess the skills that aremost needed for the customer communication.

After the customer task is routed to an initial agent, at step 412, thecontact center 100 may create a text of the initial customer-agentinteraction in step 412. The contact center 100 sends a text of thecustomer interaction to the analytics server of analytic module 320,which will then return information regarding customer personality typeto build an updated profile of the customer in near real-time at step414. Alternatively, in an unshown embodiment, the analytics center 120receives the initial customer-agent interaction, or a text thereof, forprocessing by the analytics server of analytic module 230. If thecustomer contact is a telephone call, the audio of the call can berecorded, transmitted and analyzed. If the customer communication isalready text based (on-line chat, email, social media contact, textmessage, etc.), then the text can be sent directly to the analyticsmodule 320 and processed similarly. For example, the analytics servercan analyze the extracted text to identify one or more customer issues.These issues may require attention and be routed appropriately, or maybe used to create a new prediction for that customer that is stored inthe national database or other appropriate database.

The analytics server of the analytics module 320 may use the followinginputs to create the updated customer profile: text, linguisticalgorithms (distress, personality styles, life events, engagements),previously stored results, and the results of additional analytics addedto the profile, and any combination thereof. The analytics servercreates a profile of the customer progressively based on these smallsegments during the customer interaction. The updated customer profileis sent to the routing module 315 in step 416, so that if the customerneeds to be transferred to a second agent, the routing module 315 canuse the newly updated profile of the customer to determine whichavailable agents are the best agents for the transfer.

The real-time (or near real-time) routing function of method 400 canoperate on both the initial agent assignment and possible secondarytransfer agent assignment. A communication distributor of a contactcenter assigns each incoming communication to the agent who is the bestmatch for the communications based on inputs from database module 305,the governor module 310, the routing module 315, and a databasecontaining agent personality information and customer personalityinformation. In some embodiments, inputs from the analytics module 320are also used.

Referring now to FIG. 5, illustrated is a block diagram of a system 500suitable for implementing embodiments of the present disclosure,including database module 305, governor module 310, routing module 315,and analytics module 320 depicted in FIG. 3. System 500, such as part acomputer and/or a network server, includes a bus 502 or othercommunication mechanism for communicating information, whichinterconnects subsystems and components, including one or more of aprocessing component 504 (e.g., processor, micro-controller, digitalsignal processor (DSP), etc.), a system memory component 506 (e.g.,RAM), a static storage component 508 (e.g., ROM), a network interfacecomponent 512, a display component 514 (or alternatively, an interfaceto an external display), an input component 516 (e.g., keypad orkeyboard), and a cursor control component 518 (e.g., a mouse pad).

In accordance with embodiments of the present disclosure, system 500performs specific operations by processor 504 executing one or moresequences of one or more instructions contained in system memorycomponent 506. Such instructions may be read into system memorycomponent 506 from another computer readable medium, such as staticstorage component 508. These may include instructions to associateidentifying origination data with a predicted personality type, providea list of available agents based on occupancy level and a predeterminedwork threshold, provide a routing recommendation of the best availableagent, actually route the customer communication to the best availableagent, etc. In other embodiments, hard-wired circuitry may be used inplace of or in combination with software instructions for implementationof one or more embodiments of the disclosure.

Logic may be encoded in a computer readable medium, which may refer toany medium that participates in providing instructions to processor 504for execution. Such a medium may take many forms, including but notlimited to, non-volatile media, volatile media, and transmission media.In various implementations, volatile media includes dynamic memory, suchas system memory component 506, and transmission media includes coaxialcables, copper wire, and fiber optics, including wires that comprise bus502. Memory may be used to store visual representations of the differentoptions for searching or auto-synchronizing. In one example,transmission media may take the form of acoustic or light waves, such asthose generated during radio wave and infrared data communications. Somecommon forms of computer readable media include, for example, RAM, PROM,EPROM, FLASH-EPROM, any other memory chip or cartridge, carrier wave, orany other medium from which a computer is adapted to read.

In various embodiments of the disclosure, execution of instructionsequences to practice the disclosure may be performed by system 500. Invarious other embodiments, a plurality of systems 500 coupled bycommunication link 520 (e.g., networks 102 or 104 of FIG. 1, LAN, WLAN,PTSN, or various other wired or wireless networks) may performinstruction sequences to practice the disclosure in coordination withone another. Computer system 500 may transmit and receive messages,data, information and instructions, including one or more programs(i.e., application code) through communication link 520 andcommunication interface 512. Received program code may be executed byprocessor 504 as received and/or stored in disk drive component 510 orsome other non-volatile storage component for execution.

In view of the present disclosure, it will be appreciated that variousmethods and systems have been described according to one or moreembodiments for routing incoming customer communications and tasks.

Where applicable, various embodiments provided by the present disclosuremay be implemented using hardware, software, or combinations of hardwareand software. Also where applicable, the various hardware componentsand/or software components set forth herein may be combined intocomposite components comprising software, hardware, and/or both withoutdeparting from the spirit of the present disclosure. Where applicable,the various hardware components and/or software components set forthherein may be separated into sub-components comprising software,hardware, or both without departing from the spirit of the presentdisclosure. In addition, where applicable, it is contemplated thatsoftware components may be implemented as hardware components, andvice-versa.

Software in accordance with the present disclosure, such as program codeand/or data, may be stored on one or more computer readable mediums. Itis also contemplated that software identified herein may be implementedusing one or more general purpose or specific purpose computers and/orcomputer systems, networked and/or otherwise. Where applicable, theordering of various steps described herein may be changed, combined intocomposite steps, and/or separated into sub-steps to provide featuresdescribed herein.

The foregoing outlines features of several embodiments so that a personof ordinary skill in the art may better understand the aspects of thepresent disclosure. Such features may be replaced by any one of numerousequivalent alternatives, only some of which are disclosed herein. One ofordinary skill in the art should appreciate that they may readily usethe present disclosure as a basis for designing or modifying otherprocesses and structures for carrying out the same purposes and/orachieving the same advantages of the embodiments introduced herein. Oneof ordinary skill in the art should also realize that such equivalentconstructions do not depart from the spirit and scope of the presentdisclosure, and that they may make various changes, substitutions andalterations herein without departing from the spirit and scope of thepresent disclosure.

The Abstract is provided to comply to allow a quick determination of thenature of the technical disclosure. It is submitted with theunderstanding that it will not be used to interpret or limit the scopeor meaning of the claims.

What is claimed is:
 1. A system configured to route incoming customertasks based on agent performance, comprising: a node comprising aprocessor and a non-transitory computer readable medium operably coupledthereto, the non-transitory computer readable medium comprising aplurality of instructions stored in association therewith that areaccessible to, and executable by, the processor, where the plurality ofinstructions comprises: instructions that, when executed, identifyorigination data for a customer contacting a contact center with acustomer task; instructions that, when executed, monitor real-time agentperformance data and modify a predetermined work threshold based on thereal-time agent performance data; instructions that, when executed,provide a routing recommendation to a communication distributor to routethe customer to an agent based on historical customer data, wherein theagent has not exceeded the predetermined work threshold; andinstructions that, when executed, route the communication via thecommunication distributor to the agent based on the routingrecommendation.
 2. The system of claim 1, wherein the predetermined workthreshold is lowered if the real-time agent performance data is lowerthan previously determined real-time agent performance data.
 3. Thesystem of claim 1, wherein the real-time agent performance data includesone or more of the agent's proficiency score, the agent's workload, or acombination thereof.
 4. The system of claim 2, wherein the agent'sproficiency score is for a type of task, a customer having a particularpersonality type, or both.
 5. The system of claim 1, wherein thepredetermined work threshold is set higher for a top percentage ofagents and at one or more lower values for the remaining agents.
 6. Thesystem of claim 1, the historical customer data comprises pastinteraction data with the customer, one or more previous transactionsbetween the customer and a contact center, past purchase history, pastcalling history, past survey responses, or a combination thereof.
 7. Thesystem of claim 1, which further comprises instructions that, whenexecuted, analyze one or more received, real-time interactions betweenthe customer and an initial available agent.
 8. The system of claim 7,which further comprises instructions that, when executed, create orupdate a customer profile based on the analysis.
 9. A system configuredto route incoming customer communications, comprising: a database moduleto associate identifying origination data of a customer contacting acontact center; a governor module to monitor real-time agent performancedata and modify a predetermined work threshold based on the real-timeagent performance data; a routing processor to match a customercommunication to an available agent based on historical customer datafor the customer, wherein the available agent has not exceeded thepredetermined work threshold; and a communication distributor thatroutes the customer communication to the available agent based on therouting match.
 10. An analytics center comprising the system of claim 9.11. A method for routing incoming customer communications, whichcomprises: receiving, by one or more processors, a customercommunication; monitoring, by the one or more processors, real-timeagent performance data and modifying a predetermined work thresholdbased on the real-time agent performance data; and providing, by the oneor more processors, a routing recommendation to a communicationdistributor to route the customer to an agent based on historicalcustomer data, wherein the agent has not exceeded the predetermined workthreshold.
 12. The method of claim 11, which comprises lowering thepredetermined work threshold if the real-time agent performance data islower than previously determined real-time agent performance data. 13.The method of claim 11, wherein the real-time agent performance data isselected to include one or more of the agent's proficiency score, theagent's workload, or a combination thereof.
 14. The method of claim 12,wherein the agent's proficiency score is selected to comprise a type oftask, a customer having a particular personality type, or both.
 15. Themethod of claim 11, wherein the predetermined work threshold is sethigher for a top percentage of agents and at one or more lower valuesfor the remaining agents.
 16. A system configured to simulate areal-time predictive routing system to route incoming customer tasks,which comprises: a node comprising a processor and a non-transitorycomputer readable medium operably coupled thereto, the non-transitorycomputer readable medium comprising a plurality of instructions storedin association therewith that are accessible to, and executable by, theprocessor, where the plurality of instructions comprises: instructionsthat, when executed, identify origination data for a customer contactinga contact center with a customer task; instructions that, when executed,monitor agent workload and stop assigning customer tasks to each agentat any time that the agent has exceeded a predetermined work threshold;instructions that, when executed, provide a routing recommendation toroute the customer to an agent based on historical customer data,wherein the agent has not exceeded the predetermined work threshold;instructions that, when executed, route the customer to an agent via acommunications distributor when the agent has not exceeded thepredetermined work threshold; instructions that, when executed, createan actual routing summary of the routing; and instructions that, whenexecuted, compare a recommended routing summary based on the routingrecommendations over a period of time to the actual routing summary overthe period of time, and display the comparison to a user on a userdisplay.
 17. The system of claim 16, wherein the routing recommendationsare based on a different predetermined work threshold for each agentthan the routing.
 18. The system of claim 17, wherein the differentpredetermined work threshold is based on an agent's real-timeperformance data.
 19. The system of claim 16, wherein the display showsone or more routing recommendations over the routing.
 20. The system ofclaim 16, wherein the display shows the recommended routing summary overthe actual routing summary, each for the period of time, for one or moreagents.
 21. A system configured to route incoming customercommunications, comprising: a database module to associate identifyingorigination data of a customer contacting a contact center; a governormodule to monitor real-time agent performance data and modify apredetermined work threshold based on the real-time agent performancedata; a routing processor to provide a routing recommendation to route acustomer communication to an available agent based on historicalcustomer data for the customer, wherein the available agent has notexceeded the predetermined work threshold; a communication distributorthat routes the customer communication to the available agent based onthe routing match and creates an actual routing summary of the routesover a period of time; and a simulation module that compares arecommended routing summary based on the routing recommendations overthe period of time to the actual routing summary over the period oftime, and displays the comparison to a user on a user display.
 22. Ananalytics center comprising the system of claim
 21. 23. A method forrouting incoming customer communications, which comprises: receiving, byone or more processors, a customer communication; monitoring, by the oneor more processors, real-time agent performance data and modifying apredetermined work threshold based on the real-time agent performancedata; providing, by the one or more processors, a routing recommendationto route the customer to an agent based on historical customer data,wherein the agent has not exceeded the predetermined work threshold;routing the customer to an agent when the agent has not exceeded thepredetermined work threshold; creating an actual routing summary of oneor more of the routings over a period of time; and comparing arecommended routing summary based on the routing recommendations to theactual routing summary over the period of time, and display thecomparison to a user.
 24. The method of claim 23, wherein the routingrecommendations are selected based on a different predetermined workthreshold for each agent than the routing.
 25. The method of claim 24,wherein the different predetermined work threshold is selected based onan agent's real-time performance data.
 26. The method of claim 23,wherein the display shows one or more routing recommendations over therouting for the period of time.
 27. The method of claim 23, wherein thedisplay shows the recommended routing summary over the actual routingsummary, each for the period of time, for one or more agents.